JP7212966B2 - Composite data generation method and composite data generation device - Google Patents

Description

The present invention relates to a data structure of composite data, a composite data generation method, and a composite data generation device. Specifically, the present invention relates to the data structure of composite data containing scent data.

In order to search and extract data having specific characteristics from a collection of various data, metadata associated with each data is added to each data (see Patent Document 1).

Japanese Patent Publication No. 2011-507083

Attention has been focused on the possibility that a strong correlation exists between odors and human memory. However, it was difficult to convert odors into digital data. Therefore, it has been considered difficult to search or extract a collection of data, such as digital images, based on odors, although there is a potential demand for this.

SUMMARY OF THE INVENTION The present invention has been made in view of the above circumstances, and provides a data structure and a composite data generation device that enable searching and extracting data associated with specific odor data from a set of data. Let it be an exemplary problem.

In order to solve the above problems, the present invention has the following configurations.
(1) A data structure having a main data storage area in which main data is stored and an odor data storage area in which odor data based on measurement results of odors in the air measured by an odor sensor is stored.

Further objects or other features of the present invention will be made clear by preferred embodiments described below with reference to the accompanying drawings.

According to the present invention, it is possible to provide a data structure and a composite data generation device that make it possible to search and extract data associated with specific odor data from a set of data.

4 is an explanatory diagram showing a first example of a data structure according to Embodiment 1; FIG. FIG. 8 is an explanatory diagram showing a second example of the data structure according to the first embodiment; A measurement result database D1. This is the processing database D2. It is a graph which shows the measurement result based on the processing database D2. FIG. 10 is an explanatory diagram showing an example of processing of odor measurement results; 2 is a plan view schematically showing a first example of the odor sensor 10 according to Embodiment 1. FIG. FIG. 8 is a cross-sectional view schematically showing an A-A′ cross section in FIG. 7; FIG. 4 is a plan view schematically showing a second example of the odor sensor 10 according to Embodiment 1; 4 is an explanatory diagram of the internal configuration of the odor measuring device 50. FIG. 5 is a schematic diagram illustrating introduction of air into the odor measuring device 50. FIG. 1 is a schematic diagram showing a diagnostic device 100 of Example 1. FIG. 2 is a block diagram schematically showing a first example of the internal configuration of the diagnostic device 100 of Example 1. FIG. FIG. 3 is a block diagram schematically showing a second example of the internal configuration of the diagnostic device 100 of Example 1; It is the database D11 of Example 1. FIG. 4 is a flow chart showing processing of the diagnostic device 100 of the first embodiment; FIG. 10 is a schematic diagram showing a portable information terminal 200 of Example 2; FIG. 11 is a block diagram schematically showing the internal configuration of a mobile information terminal 200 of Example 2; It is the database D21 of Example 2. FIG. 10 is a flowchart showing processing of the portable information terminal 200 of Example 2; FIG. 11 is a schematic diagram showing a video shooting terminal 300 of Example 3; FIG. 11 is a block diagram schematically showing the internal configuration of a moving image capturing terminal 300 of Example 3; It is database D31 of Example 3. FIG. FIG. 11 is a flow chart showing processing of the video shooting terminal 300 of Example 3. FIG.

[Embodiment 1]
A data structure according to the first embodiment will be described below with reference to the drawings. In Embodiment 1, the “odor” is something that humans or living organisms including humans can acquire as olfactory information, and a single molecule or a molecular group consisting of different molecules has their respective concentrations. It is a concept that includes a set of

In embodiment 1, said odor is composed of odorants. An odorant is an aggregate of various odor components, such as a single molecule or a molecular group consisting of different molecules, each having its own concentration. However, in a broad sense, the odorant may broadly mean a substance that can be adsorbed by the substance adsorption film of the odor sensor 10, which will be described later. In other words, "smell" often contains multiple odorants that are causative, and there may also be substances that are not recognized as odorants or unknown odorants. It may also include substances that are not

FIG. 1 is an explanatory diagram showing a first example of a data structure according to the first embodiment. The odor data containing data 1 having the data structure according to the first embodiment has a main data storage area 4 and an odor data storage area 6, as shown in FIG. 1(a). That is, the data structure according to the first embodiment is a data structure in which the main data 3 stored in the main data storage area 4 is stored in association with the odor data 5 stored in the odor data storage area 6 . The data structure according to the first embodiment is a data structure for odor data that enables analysis of the main data 3 based on the odor data 5 .

The main data storage area 4 stores main data 3 such as image data associated with the odor data 5, position data, moving image data, and the like. The scent data storage area 6 stores the scent data 5 associated with the main data 3 . That is, the main data 3 and the odor data 5 exist in the odor data containing data 1 in a mutually associated state. In the first embodiment, the odor data containing data 1, the main data 3, and the odor data 5 are all electronic data, and are information processed by a computer or the like.

Various types of data can be used as the main data 3 without any particular limitation. For example, image data generated by an imaging device, video data generated by a recording device, audio data generated by a recording device, text data such as sentences, position data generated by a GPS device, etc. It can be used as data 3.

The main data storage area 4 may have a main data ID area 24 . The main data ID area 24 is an area for storing the main data ID 23 . The main data ID 23 is data that indicates that the data stored in the main data storage area 4 is the main data 3 .

As the odor data 5, for example, data generated based on measurement results obtained by an odor measuring device 50 having an odor sensor 10, which will be described later, can be used. As the odor sensor 10, one having a plurality of sensor elements 11 can be used. Each sensor element 11 has a substance adsorption film 13 and a detector 15 . The substance adsorption film 13 adsorbs odorants in the air. The detector 15 detects an adsorption state in which the odorant is adsorbed on the substance adsorption film 13 . Each sensor element 11 has a different substance adsorption film 13 . That is, each sensor element 11 has a different adsorption characteristic of an odorant to the substance adsorption film 13 .

The odor data storage area 6 may have an odor data ID area 26 . The odor data ID area 26 is an area for storing the odor data ID 25 . The odor data ID 25 is data that indicates that the data stored in the odor data storage area 6 is the odor data 5 .

A single odor data 5 may be stored in the odor data storage area 6, or a plurality of odor data 5 may be stored. When a plurality of odor data 5 are stored in the odor data storage area 6, each of the plurality of odor data 5 corresponds to each of the plurality of sensor elements 11 included in the odor sensor 10 on a one-to-one basis. can be done. In the following description, each odor data 5 corresponding to each of the plurality of sensor elements 11 may be referred to as element data 7 . A plurality of element data 7 in the odor data storage area 6 may each be stored in an element data storage area 8 as shown in FIG. 1(b).

The odor data storage area 6 may include a plurality of odor data ID areas 26 . These multiple odor data ID areas 26 can be associated one-to-one with each of the multiple odor data 5 stored in the odor data storage area 6 . In the following description, when there are a plurality of odor data ID areas 26, each odor data ID area 26 may be called an element data ID area 28. FIG. An element data ID 27 is stored in each element data ID area 28 . The element data ID 27 is data indicating that the data stored in the element data ID area 28 is data corresponding to a specific sensor element 11, or the like. Each element data ID area 28 corresponds to each of the plurality of sensor elements 11 of the odor sensor 10 on a one-to-one basis.

Each element data storage area 8 is preferably arranged in line within the odor data storage area 6 . When numbers are assigned to the sensor elements, it is preferable that the element data storage areas 8 corresponding to the sensor elements of the numbers are arranged in the odor data storage area 6 in order of the assigned numbers.

The odor data 5 may be measurement results obtained by the sensor element 11 of the odor sensor 10 . That is, the odor data 5 may be raw data in which the measurement result of the odor sensor 10 is used without being processed. Specifically, the measurement result of the odor in the air measured by the odor sensor 10 may be transition data indicating the temporal change of the odor in the air.

The transition data may be measured by the odor sensor 10 at predetermined time intervals over a predetermined time span. The transition data may be composed of a plurality of data sets including measured values of odors in the air measured by the odor sensor 10 and the measurement times of the measured values.

For example, as shown in FIG. 1(c), odor data 5 (transition data) is measured in each of the sensor elements 11 over time from the measurement start time (t0) to the measurement end time (tz). The data may include multiple element data points 9 (measurements) at time (tx). Each element data point 9 (measurement value) is associated one-to-one with the data of the measurement time, and the odor data 5 (transition data) is composed of a plurality of data sets having the element data point 9 and the measurement time. there is In this specification, time (tx), time (ty), and time (tz) are arbitrary times after x seconds, y seconds, and z seconds, respectively, from the measurement start time (t0). .

The odor data 5 includes an element data point 9 at the measurement start time (t0), an element data point 9 at a time (tx) after a predetermined time (x) has elapsed from the measurement start time (t0), and an element data point 9 at the measurement end time (tz). Data points 9 may be included. Each element data point 9 is preferably arranged in the odor data storage area 6 in chronological order of measurement time. A plurality of element data points 9 at time (tx) are preferably provided. For example, when element data points 9 are acquired at intervals of 1 second from the measurement start time (t0) to the measurement end time (t40) 40 seconds later, the element data points 9 at time (tx) are x from 1 to 39 39 points are obtained.

The element data storage area 8 may have a time label area 30 for storing a time label 29 corresponding to each element data point 9, as shown in FIG. 1(c). That is, a time label area 30 for storing a time label 29 indicating at which time (tx) each element data point 9 was measured is located in the element data storage area 8 . may The time label areas 30 are preferably arranged in chronological order within the element data storage area 8, but since the time labels 29 are applied, each element data point 9 is random regardless of the chronological order. may be placed in

As the element data 7 , instead of the raw data described above, processed data processed by the arithmetic processing unit (CPU) 51 provided in the odor sensor 10 may be used. For example, a set of element data points 9 at each measurement time (tx) in the raw data can be processed according to predetermined rules. The arithmetic processing may be a process of detecting the maximum value and the minimum value from the set of element data points 9 , calculating the difference (absolute value), and using the value as the element data 7 . Further, the arithmetic processing may be a process of calculating an average value such as an arithmetic mean value, a median value, or the like from a set of element data points 9 and using the value as the element data 7 . When processed data is used as the element data 7, basically only one data corresponding to each sensor element 11 is required. Therefore, a large amount of information can be efficiently stored as odor data without bloating the data. It can be stored in area 6.

FIG. 2 is an explanatory diagram showing a second example of the data structure according to the first embodiment. As shown in FIGS. 2A to 2C, the data structure according to the first embodiment does not have a main data ID area 24, an odor data ID area 26, an element data ID area 28, and a time label area 30. It can also be structured. In this case, the main data 3 and the odor data 5 can be stored in the main data storage area 4 and the odor data storage area 6, respectively, which are determined in advance as the predetermined areas of the data 1 containing the odor data.

Each element data 7 can be aligned and stored in the odor data storage area 6 in a predetermined order. When each sensor element 11 is assigned a number, each element data 7 can be stored in the order of the number. Further, when each sensor element 11 is assigned a number, the order in which the sensor elements 11 are arranged in the odor data storage area 6 is associated with the number of the corresponding sensor element 11 and stored in the storage device 53 or the like. If there is, each element data 7 need not be arranged in numerical order of the sensor elements 11 . Each element data point 9 can be stored aligned in chronological order in which it was measured.

Two examples of the data structure according to the first embodiment are shown using FIG. 1 (first example) and FIG. 2 (second example), but the data structure is not limited to these. In the first example, a data structure that does not have at least one of the main data ID area 24, the scent data ID area 26, the element data ID area 28, and the time label area 30 is also possible. In the second example, a data structure having at least one of the main data ID area 24, the scent data ID area 26, the element data ID area 28, and the time label area 30 is also possible.

Next, the process of acquiring an odor by the odor sensor 10 will be described. Acquisition of an odor by the odor sensor 10 can be realized by steps of a measurement result acquisition step S1 and a data processing step S2.

<Measurement result acquisition step S1>
In the measurement result acquisition step S1, the odor sensor 10 is used to acquire each measurement result of the odorant contained in the sample in each of the plurality of sensor elements 11 included in the odor sensor 10 . The plurality of sensor elements 11 have different detection characteristics with respect to odorants. A specific configuration of the odor sensor 10 will be described later.

Each measurement result is acquired in association with each of the plurality of sensor elements 11 . Specifically, it can be obtained as a measurement result database in which each sensor element 11 and each measurement result measured by each sensor element 11 are stored in a mutually associated state.

FIG. 3 shows the measurement result database D1. In the measurement result database D1, each sensor element 11 and each measurement result measured by each sensor element 11 are stored in a mutually associated state. In the measurement result database D1 shown in FIG. 3, measurement results of a total of 35 sensor elements 11, ie, sensor elements 11-01 to 11-35, are stored in association with each other. Note that the sensor elements 11-06 to 11-34 are omitted in FIG. 3 for convenience of explanation. The measurement result database D1 shown in FIG. 3 shows the measurement results (data point 9) when the odor sensor 10 is brought into contact with the odor to be measured during the time t from 15 seconds to 20 seconds. be.

Specifically, the measurement results are raw data detected by each sensor element 11 . If the odor sensor 10 is, for example, a quartz crystal oscillator sensor (QCM), the raw data generated by the sensor element 11 can be the change over time of the resonance frequency of the quartz crystal oscillator. That is, the results of measurement by the sensor element 11 can be the resonance frequencies (data points 9) at a plurality of different points in time elapsed from the start of operation of the odor sensor 10. FIG. For example, as shown in FIG. 3, the measurement result (data point 9) measured by the sensor element 11-01 shows that the resonance frequency measured after 0 seconds (t0) is "16101528 Hz" and after 17 seconds ( The resonance frequency measured at t17) is "16101515 Hz". In addition, the measurement results (data point 9) measured by the sensor element 11-02 show that the resonance frequency measured 0 seconds (t0) after the start of operation of the odor sensor 10 is "16081740 Hz", and after 16 seconds ( The resonance frequency measured at t16) is "16081728 Hz". In the measurement, the time interval for recording the measurement results is not particularly limited, but it can be, for example, one second interval.

The time width from the start to the end of operation of the odor sensor 10 is not particularly limited, but it is preferably longer than the time width during which the odor sensor 10 is brought into contact with the odor to be measured. Further, the start of operation of the odor sensor 10 is preferably several seconds or more, preferably 5 seconds or more, before the odor sensor 10 is brought into contact with the odor to be measured (odor measurement). The end of the operation of the odor sensor 10 is preferably several seconds or more after the odor sensor 10 is brought into contact with the odor to be measured, preferably 5 seconds or more. In the measurement result database D1 shown in FIG. 3, the start of operation of the odor sensor 10 is 15 seconds (t0) before the start of odor measurement, and the end of operation of the odor sensor 10 is 20 seconds (t40) after the end of odor measurement. is.

It is preferable that the measurement by the odor sensor 10 is performed a plurality of times, and the average value of the raw data of the measurements performed a plurality of times is used as the measurement result. The number of measurements is not particularly limited, but may be three, for example. As the average value, an arithmetic average (arithmetic average) can be used.

<Data Processing Step S2>
In the data processing step S2, the measurement results acquired in the measurement result acquisition step S1 are respectively processed to generate odor data 5 (processed data) associated with each of the plurality of sensor elements 11. FIG.

The processed data may be the measurement result of the odor in the air measured by the odor sensor 10, and may be the average value or the median value of the measurement result in a predetermined time span. The processed data is not limited to these, and may be processed data as described below.

(Difference data based on t0)
The odor data 5 is the measurement result of the odor in the air measured by the odor sensor 10, and is the first time or time span of the odor in the air for indicating the temporal change in the odor in the air. It may be difference data between the first measurement result and the second measurement result of the odor in the air at the second time or time span. For example, the odor data 5 may be the difference between a data point 9 at a predetermined time (tx) and a data point 9 at a predetermined time (ty) as processed data.

FIG. 4 shows the processing database D2. As shown in FIG. 4, the odor data 5 may be obtained by subtracting the value at time 0 (t0) as difference data from each measurement result of the time span (t1 to t40) after time 0. FIG. That is, the odor data 5 may be difference data based on the measurement result at time 0 (t0).

(Differential data between two different points)
The difference data is the difference between the first measurement result, which is the measurement result of the odor in the air at the first time, and the second measurement result, which is the measurement result of the odor in the air at the second time. It may be data. At this time, the time difference between the first time and the second time is preferably at least several seconds, more preferably at least 5 seconds, and particularly preferably at least 10 seconds. For example, difference data is acquired between two points separated by an interval of several seconds or more between the time when the odorant to be measured can contact the odor sensor 10 and the time when it cannot contact. As a result, as the odor data 5, the characteristics of the odor can be made clearer.

Such differential data between two different points can be, for example, the difference between the measurement result (data point 9) at each time and the measurement result (data point 9) at time 0 (t0).

The difference data between two different points is, for example, the difference (absolute value) between the maximum value of the measurement results in a predetermined time period and the minimum value of the measurement results in the predetermined time period. can also Here, the predetermined time width including the maximum value and the predetermined time width including the minimum value may or may not overlap. or all of them may be duplicated. The predetermined time width may be the time width for odor measurement, or may be a time width other than the time width for odor measurement (time width for background measurement). Specifically, the difference between the maximum value of the measurement results in the odor measurement time width and the minimum value in the time width (background) other than the odor measurement time width can be used as the difference data. The difference between the maximum value and the minimum value in the time span of odor measurement can be used as the difference data. The difference between the minimum value of the measurement results in the odor measurement time width and the maximum value in the time width (background) other than the odor measurement time width can be used as difference data. The difference between the minimum value and the maximum value in the odor measurement time span among the measurement results in the odor measurement time span can also be used as difference data.

The difference data can also be the difference (absolute value) between the maximum or minimum value of the measurement results in a predetermined time width and the average or median value of the measurement results in the predetermined time width. .

As the difference data, when the adjustment devices 55, 57, 59, etc. are arranged in the odor measurement device 50 as described later, when air containing the odorant of the odor to be measured is introduced from the introduction port 56, The difference between the measurement result when the adjusting device 55 is open and the measurement result when the adjusting device 55 is closed can be used as difference data. The regulating devices 55, 57, 59 can be fans, shutters, sealing valves, or the like. If the adjustment device 55 is a fan, the measurement results obtained when the fan rotates (normally) in the direction of introducing the air containing the odorant to be measured, and whether the fan is stopped or rotated in the opposite direction. The difference between the measurement result in the rotating (reversing) state and the measurement result can be used as difference data.

As difference data, for the measurement results (raw data) acquired in the measurement result acquisition step S1, the maximum value and the first minimum value after passing through the maximum value (hereinafter also referred to as "minimum value immediately after the maximum value"). , can also be the difference between If there are a plurality of such differences (a maximum value and a minimum value immediately after that), the maximum difference value (absolute value) is taken as the difference of the measurement results. In this way, for each measurement result, the difference associated with each of the plurality of sensor elements 11 is obtained.

FIG. 5 is a graph showing measurement results based on the processing database D2. In the graph of FIG. 5, the difference data is the difference between the maximum value and the minimum value immediately after the maximum value. In FIG. 5, the vertical axis represents the amount of displacement [Hz] of the resonance frequency measured after a predetermined time from the resonance frequency at 0 seconds after the start of operation of the odor sensor 10, and the horizontal axis represents the odor. Elapsed time [seconds] after the start of operation of the sensor 10 . FIG. 5 shows the measurement results for the sensor elements 11-01, 11-02, and 11-03 among the measurement results shown in the processing database D2. In FIG. 5, the measurement result of the sensor element 11-01 is indicated by a solid line, the measurement result of the sensor element 11-02 is indicated by a broken line, and the measurement result of the sensor element 11-03 is indicated by a dashed line. Needless to say, graphs can be similarly created for the other sensor elements 11-04 to 11-35. In FIG. 5, for the sensor element 11-01, the difference in the measurement results is "22 Hz". That is, in the measurement results of the sensor element 11-01, the maximum value "9 Hz" at 14 seconds after the start of operation of the odor sensor 10 and the minimum value "- 13 Hz".

When calculating the difference, the range of elapsed time after the start of operation of the odor sensor 10 used as the odor measurement result may be limited. For example, when the measurement of the odor of the sample is started 15 seconds after the start of operation of the odor sensor 10, and the measurement of the odor of the sample is finished 20 seconds after the start of operation of the odor sensor 10, the elapsed time for calculating the difference is can be set to the elapsed time from the start of operation of the odor sensor 10 from 14 seconds to 25 seconds. Note that the range of this elapsed time can be set arbitrarily.

As the processed data, a logarithmic operation may be performed on each of the calculated differences, and the logarithmic value associated with each of the plurality of sensor elements 11 may be used as the odor data 5 (element data 7). In logarithmic operation, the base is not particularly limited, but may be 2, for example.

As processed data, simplified data such as data obtained by classifying measurement results and flagging according to the classification may be used. For example, logarithmic values obtained by logarithmic operations as described above can be classified into a plurality of regions according to the magnitude of the values. Although the number of regions to be classified is not particularly limited, it can be, for example, 3 to 5 regions. A case of classifying into three areas will be described below.

First, the maximum and minimum logarithmic values for each measurement result are identified. The quotient of the difference divided by 3 between the largest and smallest logarithmic values is then calculated. The quotient thus obtained can be used to partition the numerical range between the maximum and minimum logarithmic values into three equal regions. That is, the area from the minimum logarithm to the minimum logarithm plus the quotient, the area from the minimum logarithm plus the quotient to the minimum logarithm plus twice the quotient, and and the area from the minimum logarithm plus twice the quotient to the maximum logarithm.

Each of the logarithmic values associated with each sensor element 11 is then classified into one of three regions. For each logarithmic value, a flag may be provided to identify the classified region. For example, flags such as (1), (2), and (3) can be provided in ascending order of value for three areas divided into three equal parts. Thereby, the measurement result associated with each sensor element 11 can be classified into three stages according to the magnitude of the value.

FIG. 6 is an explanatory diagram showing an example of processing the odor measurement results. Table (A) in FIG. 6 is a table showing differences calculated for a certain sample. Difference values are shown for each of the sensor elements 11-01 to 11-35. For example, in Table (A), the difference obtained for the sensor element 11-01 is "38.7" and the difference obtained for the sensor element 11-02 is "27.0". For convenience of explanation, the values of the sensor elements 11-11 to 11-34 are omitted (the same applies to Tables (B) and (E) described later).

Next, the difference for each sensor element 11 is logarithmically processed. The logarithmic operation here is represented by the following formula (1). That is, the absolute value of the difference is logarithmically calculated with a base of 2 to obtain a logarithmic value.
[logarithmic value]=log ₂ |[difference]| Expression (1)

Table (B) is a table showing logarithmic values for each sensor element 11 . For example, in Table (B), the logarithmic value calculated based on the difference obtained in sensor element 11-01 is "5.3", and the logarithmic value calculated based on the difference obtained in sensor element 11-02 is The logarithmic value obtained is "4.8".

A value classification substep S2-3 then classifies the logarithmic values for each sensor element 11 into three regions based on the obtained logarithmic values. Specifically, first, the maximum (maximum value) and the minimum (minimum value) of logarithmic values for each sensor element 11 are identified in the sample being measured. Then, the quotient when the difference between the maximum value and the minimum value is divided by 3 is calculated. These identified maximum values, minimum values, and calculated quotients are shown in Table (C). In Table (C), the specified maximum value is "6.7", the specified minimum value is "3.1", and the calculated quotient is "1.2".

Based on the specified maximum value, minimum value, and calculated quotient, the logarithmic value of each sensor element 11 is classified into three stages. Classification is based on classification rules as shown in Table (D). Specifically, the region with the smallest logarithmic value (region 1) has a range of 3.1 ≤ [logarithmic value] ≤ 4.3, and the region with the next smallest logarithmic value (region 2) has a range of 4.3 < [ logarithmic value]≤5.5, and the region with the largest logarithmic value (region 3) is classified based on the classification rule of 5.5<[logarithmic value]≤6.7.

Next, a flag is assigned to each sensor element 11 based on the classified results. Table (E) shows the results of assigning flags to each sensor element 11 . The sensor elements 11 for which the logarithmic value corresponding to region 1 was obtained are flagged (1), the sensor elements 11 for which the logarithmic value corresponding to region 2 were obtained are flagged (2), and the pair corresponding to region 3 are flagged (1). A flag (3) is assigned to the sensor element 11 for which a numerical value is obtained. For example, in Table (E), flag (2) is assigned to sensor element 11-01, flag (1) is assigned to sensor element 11-30, and flag (3) is assigned to sensor element 11-09. Granted.

<Odor sensor 10>
FIG. 7 is a plan view schematically showing a first example of the odor sensor 10 according to Embodiment 1. FIG. 2 is a plan view schematically showing the odor sensor 10 according to Embodiment 1. FIG. FIG. 8 is a cross-sectional view schematically showing the AA' cross section in FIG. The odor sensor 10 includes multiple sensor elements 11 and a sensor substrate 17 . Each sensor element 11 has a substance adsorption film 13 that adsorbs an odorant, and a detector 15 that detects the state of adsorption of the odorant to the substance adsorption film 13 .

As shown in FIG. 8, the sensor element 11 is composed of a detector 15 and a substance adsorption film 13 provided on the surface of the detector 15 . The substance adsorption film 13 preferably covers the entire surface of the detector 15 . That is, the size of the detector 15 is preferably the same as the formation range of the substance adsorption film 13 or smaller than the formation range of the substance adsorption film 13 . A plurality of detectors 15 may be provided within the formation range of one substance adsorption film 13 .

A plurality of sensor elements 11 are arranged on the sensor substrate 17 and aligned as shown in FIG. At this time, the substance adsorption films 13 of the adjacent sensor elements 11 are not in contact with each other or are insulated. It should be noted that the sensor elements 11 do not necessarily have to be aligned on the sensor substrate 17, and may be arranged randomly, or may be aligned in a form other than 3 rows and 3 columns. The number of sensor elements 11 on the sensor substrate 17 is not particularly limited. The number may be 7 as shown in FIG. 7, or, for example, 35 in 7 rows and 5 columns as shown in FIG. FIG. 9 is a plan view schematically showing a second example of the odor sensor 10 according to Embodiment 1. FIG.

The plurality of sensor elements 11 arranged on the sensor substrate 17 have different properties of the respective substance adsorption films 13 . Specifically, it is preferable that all of the plurality of sensor elements 11 are composed of substance adsorption films 13 having different compositions, and that substance adsorption films 13 having the same properties do not exist. Here, the properties of the substance adsorption film 13 can also be said to be the adsorption characteristics of the odorant to the substance adsorption film 13 . That is, even for the same odorant (or an aggregate thereof), the substance adsorption films 13 having different properties exhibit different adsorption characteristics. 7 to 10, for the sake of convenience, all the substance adsorption films 13 are shown in the same way, but actually their properties are different from each other. Note that the adsorption characteristics of the substance adsorption films 13 of the sensor elements 11 do not necessarily have to be different. may be

As a material for the substance adsorption film 13, a thin film formed of a π-electron conjugated polymer can be used. This thin film can contain at least one of inorganic acids, organic acids, and ionic liquids as a dopant. By changing the type and content of the dopant, the properties of the substance adsorption film 13 can be changed.

The π-electron conjugated polymer is not particularly limited, but is a high polymer having a π-electron conjugated polymer skeleton such as polypyrrole and its derivatives, polyaniline and its derivatives, polythiophene and its derivatives, polyacetylene and its derivatives, polyazulene and its derivatives. Molecules are preferred.

When the π-electron conjugated polymer is in an oxidized state and the skeleton polymer itself becomes a cation, conductivity can be expressed by incorporating an anion as a dopant. In the present invention, a neutral π-electron conjugated polymer containing no dopant can also be used as the substance adsorption film 13 .

Specific examples of dopants include inorganic ions such as chloride ions, chloride oxide ions, bromide ions, sulfate ions, nitrate ions, and borate ions; organic acid anions such as alkylsulfonic acid, benzenesulfonic acid, and carboxylic acid; Acids, polymer acid anions such as polystyrene sulfonic acid, and the like can be mentioned.

There is also a method of doping in chemical equilibrium by allowing a neutral π-electron conjugated polymer to coexist with a salt such as common salt or an ionic compound containing both cations and anions such as an ionic liquid. can be used.

The content of the dopant in the π-electron conjugated polymer is preferably in the range of 0.01 to 5, where 1 is the state in which one dopant unit (ion) enters per two repeating units constituting the π-electron conjugated polymer. may be adjusted in the range of 0.1 to 2. By making the content of the dopant equal to or higher than the lowest value in this range, it is possible to suppress the disappearance of the properties of the substance adsorption film 13 . Also, if the content of the dopant is less than the maximum value of this range, the effect of the adsorption property of the π-electron conjugated polymer itself is reduced, making it difficult to prepare the substance adsorption film 13 having desirable adsorption properties. can be prevented from becoming In addition, since the dopant, which is usually a low-molecular-weight substance, is dominant in the film, it is possible to suppress a drastic decrease in the durability of the substance adsorption film 13 . Therefore, by setting the content of the dopant within the range described above, it is possible to preferably maintain the detection sensitivity of the odorant.

Different types of π-electron conjugated polymers can be used in the plurality of sensor elements 11 in order to change the adsorption characteristics of the substance adsorption films 13 respectively. Also, by using the same type of π-electron conjugated polymer and changing the type and content of the dopant, different adsorption characteristics may be expressed. For example, the hydrophobic/hydrophilic performance of the substance adsorption film 13 can be changed by changing the type of π-electron conjugated polymer and the type and content of the dopant.

The thickness of the substance adsorption film 13 can be appropriately selected according to the characteristics of the odorant to be adsorbed. For example, the thickness of the substance adsorption film 13 can range from 10 nm to 10 μm, preferably from 50 nm to 800 nm. If the thickness of the substance adsorption film 13 is less than 10 nm, sufficient sensitivity may not be obtained. Also, if the thickness of the substance adsorption film 13 exceeds 10 μm, the upper limit of the weight detectable by the detector 15 may be exceeded.

The detector 15 is a signal conversion unit that measures changes in the physical, chemical, or electrical properties of the substance adsorption film 13 due to the odorant adsorbed on the surface of the substance adsorption film 13 and outputs the measurement result as, for example, an electric signal. It has a function as a (transducer). That is, the detector 15 detects the adsorption state of the odorant on the surface of the substance adsorption film 13 . Signals output by the detector 15 as measurement results include physical information such as electrical signals, light emission, changes in electrical resistance, and changes in vibration frequency.

The detector 15 is not particularly limited as long as it is a sensor that measures changes in the physical, chemical, or electrical properties of the substance adsorption film 13, and various sensors can be used as appropriate. As the detector 15, specifically, a quartz crystal sensor (QCM), a surface acoustic wave sensor, a field effect transistor (FET) sensor, a charge-coupled device sensor, a MOS field effect transistor sensor, a metal oxide semiconductor sensor, an organic conductive organic polymer sensors, electrochemical sensors, and the like.

When a crystal oscillator sensor is used as the detector 15, although not shown, electrodes may be provided on both sides of the crystal oscillator as excitation electrodes, or separate electrodes may be provided on one side to detect a high Q value. may be provided. Further, the excitation electrodes may be provided on the sensor substrate 17 side of the crystal oscillator with the sensor substrate 17 interposed therebetween. The excitation electrodes can be made of any conductive material. Specific examples of materials for the excitation electrodes include inorganic materials such as gold, silver, platinum, chromium, titanium, aluminum, nickel, nickel-based alloys, silicon, carbon, and carbon nanotubes, and conductive polymers such as polypyrrole and polyaniline. organic materials.

The shape of the detector 15 can be a flat plate shape as shown in FIG. The shape of the flat plate surface can be circular as shown in FIG. 7, but can be various shapes such as quadrangle, square, and ellipse. Further, the shape of the detector 15 is not limited to a flat plate shape, and the thickness thereof may vary, and concave portions and convex portions may be formed.

When the detector 15 uses a vibrator such as the above-described crystal vibrator sensor, by changing the resonance frequency of each vibrator in the plurality of sensor elements 11, the detector 15 can coexist on the same sensor substrate 17. It is possible to reduce the influence (crosstalk) received from other vibrators. The resonant frequency can be arbitrarily designed so that each transducer on the same sensor substrate 17 exhibits different sensitivity to a certain frequency. The resonance frequency can be changed, for example, by adjusting the vibrator or the thickness of the substance adsorption film 13 .

As the sensor substrate 17, a silicon substrate, a quartz crystal substrate, a printed wiring board, a ceramic substrate, a resin substrate, or the like can be used. The substrate is a multilayer wiring substrate such as an interposer substrate, and excitation electrodes and mounting wirings for vibrating the crystal substrate, and electrodes for conducting electricity are arranged at arbitrary positions.

With the configuration as described above, it is possible to obtain an odor sensor 10 having a plurality of sensor elements 11 each having a substance adsorption film 13 having different odor substance adsorption characteristics. As a result, when the odor sensor 10 measures the odor of air containing a certain odorant or its composition, the substance adsorption film 13 of each sensor element 11 is similarly contacted with the odorant or its composition. Each substance adsorption film 13 adsorbs an odorant in a different manner. That is, each substance adsorption film 13 has a different adsorption amount of the odorant. Therefore, the detection result of the detector 15 is different for each sensor element 11 . Therefore, for a certain odorant or its composition, the number of measurement results by the detector 15 is generated by the number of sensor elements 11 (substance adsorption films 13) provided in the odor sensor 10. FIG.

A measurement result output by the odor sensor 10 by measuring a certain odorant or its composition is usually specific (unique) to a particular odorant or odorant composition. Therefore, by measuring the odor with the odor sensor 10, it is possible to identify the odor as an odorant alone or as a composition (mixture) of odorants.

<Odor measuring device 50>
Next, an odor measuring device 50 including the odor sensor 10 will be described. FIG. 10 is an explanatory diagram of the internal configuration of the odor measurement device 50. As shown in FIG. The odor measuring device 50 has an odor sensor 10 , an arithmetic processing device 51 connected to the odor sensor 10 , and a storage device 53 connected to the arithmetic processing device 51 . The measurement results obtained by the odor sensor 10 are processed by the arithmetic processing device 51 and can be stored in the storage device 53 in the form of odor data containing data together with the main data 3 as the odor data 5 . For odor measurement, the program P1 stored in the storage device 53 is executed by the arithmetic processing device 51, whereby the odor measurement device can function as odor measurement means. It should be noted that acquisition of odor data may be performed by other configurations instead of execution by the arithmetic processing unit 51 . In Embodiment 1, the arithmetic processing unit 51 is, for example, a central processing unit (CPU), a microprocessor (MPU), etc., and the storage device 53 is, for example, a hard disk drive (HDD), a solid state drive (SDD), a memory (RAM), etc.

When measuring an odor using the odor measuring device 50, it is preferable to measure the odor in the absence of the odor to be measured in order to acquire the background. By acquiring the background and subtracting it from the odor measurement results, it is possible to more accurately detect the influence of the odor of the measurement target.

When acquiring the background, the state in which the odor to be measured does not exist is, for example, a state in which the odor sensor 10 cannot detect the odor to be measured, or the amount detected by the odor sensor 10 is negligibly small. be able to. Specifically, the source of the odor to be measured may not exist in the space where the odor sensor 10 is installed, or may be physically isolated.

As a configuration for physically isolating the odor sensor 10 from the source of the odor to be measured, for example, an adjusting device 55 as shown in FIG. 11 can be used. 11A and 11B are schematic diagrams illustrating introduction of air into the odor measuring device 50. FIG. In FIG. 11, it is assumed that the odor to be measured diffuses from the direction of the arrow. At this time, the air containing the odor to be measured is introduced into the odor measuring device 50 through the inlet 56 that opens toward the source of the odor to be measured, and reaches the odor sensor 10 . An adjustment device 55 is provided between the introduction port 56 and the odor sensor 10 . The adjustment device 55 is a device that adjusts the inflow and outflow of air. Examples of the adjusting device 55 include a sealing valve that can be opened and closed, a fan, and the like. In FIG. 11, the outer shape of the odor measuring device 50 is simplified and shown as a rectangle for convenience of explanation, but the outer shape of the odor measuring device 50 is not limited to this.

As shown in FIG. 11, the odor measurement device 50 preferably has a ventilation port 58 that opens in a direction different from that of the introduction port 56, that is, in a direction different from the direction in which the odor to be measured is introduced. By introducing air from a direction different from the introduction direction of the odor to be measured, air that does not contain the odor to be measured or whose detection amount is negligibly small can be introduced into the odor sensor 10 . . This makes it possible to obtain a more appropriate background value. A regulator 57 may be arranged between the ventilation opening 58 and the odor sensor 10 .

The opening other than the introduction port such as the ventilation port 58 is not limited to the extension of the straight line connecting the introduction port 56 and the odor sensor 10 like the ventilation port 58, but in a direction different from the introduction direction of the odor to be measured. It is sufficient to have it, and for example, it may be the ventilation port 60 or the like. The vent 58 and the vent 60 may coexist. An adjustment device 59 may also be arranged between the ventilation opening 60 and the odor sensor 10 .

The adjusting devices 55, 57, and 59 are not particularly limited as long as they can adjust the inflow and outflow of air. can be When the adjustment devices 55, 57, 59 coexist, they may each have the same configuration, but they may also have different configurations. Also, the adjustment devices 55, 57, 59 may operate in conjunction with each other. For example, if regulators 55, 57, and 59 are all sealing valves and regulator 55 is open, regulator 57 is open and regulator 59 is closed. preferable. As a result, the air introduced from the introduction port 56 is discharged from the ventilation port 58 after passing through the odor sensor 10 . On the other hand, when adjusting device 55 is in the closed state, preferably adjusting device 57 is in the closed state and adjusting device 59 is in the open state. As a result, when acquiring a background measurement value, air that does not contain the odor to be measured can be introduced from the ventilation port 60 and the odor sensor 10 can measure the odor.

As a configuration for physically isolating the odor sensor 10 from the source of the odor to be measured, a configuration in which the position of the odor sensor 10 is variable may be adopted. For example, only when the odor sensor 10 is arranged at the tip of the arm and the odor to be measured is to be measured, the odor sensor 10 can be moved to the vicinity of the source of the odor to be measured. When acquiring the background, the result of odor measurement at a location sufficiently distant from the source of the odor to be measured can be used.

<Compound data generator>
The composite data generation device includes main data generation means for generating main data 3 and an odor sensor 10 . The composite data generation device is a device capable of generating the odor data containing data 1 as composite data based on the data structure described above.

The composite data generation device will be specifically described below using examples.

[Example 1]
As Example 1, a case where the composite data generation device is the diagnosis device 100 and the main data is image data will be described. As Example 1, a case where the image data is data of a computer tomography image (CT image) will be described with reference to the drawings. Note that the image is not limited to a CT image, and may be an X-ray image obtained by an X-ray imaging device or various images obtained by image diagnosis using other radiation.

FIG. 12 is a schematic diagram showing the diagnostic device 100 of the first embodiment. A diagnostic apparatus 100 in FIG. 12 is specifically a computed tomography apparatus (CT apparatus). The diagnostic apparatus 100 includes a cylindrical gantry 111 and a bed 117 that allows at least part of the subject's body to be transported to the cylindrical hollow portion of the gantry 111 and inserted into the hollow portion. The bed 117 is configured to be able to move parallel in the direction of insertion into the hollow portion of the gantry 111 . This translation can be translated so as to be inserted into the hollow portion of the gantry 111 with the subject lying on the bed 117 . The gantry 111 has an X-ray irradiation device 112 and an X-ray detection device 113 inside. X-rays emitted from the X-ray irradiation device 112 are applied to the subject moved to the hollow portion of the gantry 111 , and the X-rays transmitted through the subject are detected by the X-ray detection device 113 . Thereby, a diagnostic image of the subject can be obtained. A diagnostic image can be output as image data 103 .

The diagnostic apparatus 100 has an odor measurement device 115 as part of the gantry 111 . The odor measuring device 115 can detect bad breath and body odor emitted by the subject. For example, the odor measuring device 115 can be arranged near the hollow portion of the gantry 111 and exposed on the surface of the housing of the gantry 111 . In this case, the odor when the subject is not in the examination room can be detected as the background odor.

The odor measurement device 115 may have the introduction port 56 described above, the ventilation ports 58 and 60, the adjustment devices 55, 57 and 59, and the like. When the odor measurement device 115 is arranged inside the gantry 111, the introduction port 56 opens toward the hollow portion of the gantry 111, that is, the portion into which the subject is inserted. In that case, the ventilation openings 58 , 60 open in a direction different from the hollow portion of the gantry 111 , that is, toward the outside of the gantry 111 .

The odor measuring device 115 is not limited to the gantry 111 and may be placed at any position as long as it can detect the bad breath and body odor of the subject. For example, the odor measurement device 115 may be arranged on the bed 117 or may be arranged on a separate arm independent from the gantry 111 and the bed 117 .

The odor measuring device 115 has a plurality of sensor elements 155a, 155b, 155c. For convenience of explanation, only three sensor elements 155a, 155b, and 155c are shown in FIG. 13, but the number of sensor elements is not limited to three.

FIG. 13 is a block diagram schematically showing a first example of the internal configuration of the diagnostic device 100 of the first embodiment. As shown in FIG. 13, in the diagnostic apparatus 100, an arithmetic processing unit (CPU) 131, a storage device 133, a gantry 111, a bed 117, and an odor measurement device 115 are connected so as to be able to communicate with each other. there is The CPU 131 can control operations of the storage device 133 , the gantry 111 , the bed 117 and the odor measuring device 115 . The storage device 133 stores a program P11 and a database D11. By executing the program P1, the CPU 131 enables the diagnostic apparatus 100 to exhibit a function of acquiring a diagnostic image, a function of measuring the odor emitted by the subject, and the like.

The CPU 131 can control the parallel movement of the bed 117 on which the subject is seated, and move the bed 117 so that the diagnostic region is inserted into the X-ray irradiation area located in the hollow portion of the gantry 111 . Next, the CPU 131 controls the X-ray diagnostic apparatus of the gantry 111 to irradiate X-rays toward the diagnostic region of the subject. The transmitted X-rays are detected by a detector on the gantry 111 . The image data 103 of diagnostic images are stored in a predetermined area of the database D11 in the storage device 133 by the CPU 131 . Thereby, the diagnostic apparatus 100 can acquire a diagnostic image.

The CPU 131 controls the odor measuring device 115 by executing the program P11, and simultaneously with the acquisition of the diagnostic image described above, or before and after acquiring the diagnostic image, the subject's bad breath, body odor, or both are detected by the odor measuring device. 115 can be used to acquire and measure. As the odor measurement device 115, the above odor sensor 10 can be used. The CPU 131 can generate the odor data 105 based on the measurement results obtained by the odor measurement device 115 by executing the program P11.

FIG. 14 is a block diagram schematically showing a second example of the internal configuration of the diagnostic device 100 of the first embodiment. As shown in FIG. 14, the CPU 131 executes the program P1 to generate the odor data 105. The CPU 151 included in the odor measuring device 115 executes the program Pa stored in the storage device 153 without using the CPU 131 as shown in FIG. can be realized by Thereby, the processing of the measurement results by the sensor elements 155 a , 155 b , 155 c and the like of the odor sensor 116 can be completed within the odor measurement device 115 .

The odor measurement device 115 has an arithmetic processing unit (CPU) 151 and a storage device 153 in addition to the sensor elements 155a, 155b, 155c and the like. The CPU 151 can acquire measurement results obtained by the sensor elements 155a, 155b, 155c, etc., and store them in the storage device 153. FIG. Note that the CPU 151 may transfer the measurement result to the CPU 131 of the diagnostic device 100 and store it in the storage device 133 without storing the measurement result in the storage device 153 .

The storage device 133 stores a database D11 in which various types of information incidental to CT examination such as subject names, diagnosis date and time, diagnostic images, odor data, diagnostic results, symptoms/cases, etiologies, etc. are stored in association with each other. It is The odor data 105 stored in the database D11 may be the odor measurement result itself (raw data) acquired by the odor sensor 116, or odor data processed based on the odor measurement result. By correlating the odor data 105 and various information associated with the CT examination in this way, it is possible to analyze the relationship between the odor, diagnosis, and symptoms. For example, if the odor data of a subject who has undergone a specific diagnosis has a characteristic, the subject whose body odor or breath odor has the characteristic odor data is likely to receive the specific diagnosis. There is.

FIG. 15 is the database D11 of the first embodiment. The database D11 stores odor data 5, subject name, date and time of diagnosis, definitive diagnosis, symptom/case, and etiology data in association with diagnostic image data obtained by CT examination. A plurality of odor data 5 may be stored in the database D11 according to before and after the treatment, the part whose odor is to be measured, or the like.

The database D11 may be stored not only in the storage device 133 but also in a cloud server to which the diagnostic device 100 is connected via the Internet. When the database D11 is stored in a cloud server, the diagnostic device 100 may directly store each piece of information stored in the database D11 in the database D11 on the cloud server without storing it in the storage device 133.

Here, the flow of processing for CT examination and acquisition of odor data 105 in diagnostic apparatus 100 will be described with reference to FIG. 16 . FIG. 16 is a flow chart showing processing of the diagnostic device 100 of the first embodiment. In step (hereinafter “S”) 1001, diagnostic device 100 starts acquiring image data 103, and odor measurement device 115 starts odor measurement. Specifically, the CPU 131 controls the X-ray irradiation device 112 , the X-ray detection device 113 , the bed 117 and the odor measurement device 115 . When the CT examination is started, the CPU 131 activates the odor measuring device 115 and the odor sensor 116 measures the odor. Next, the CPU 131 operates the bed 117 , the X-ray irradiation device 112 and the X-ray detection device 113 to obtain a CT image as the image data 103 . Note that the smell measurement by the smell measuring device 115 and the acquisition of the image data 103 may be started without waiting for the completion of one process, and the operation time periods may overlap. Also, acquisition of the image data 103 may be started prior to odor measurement.

In S<b>1002 , the CPU 131 processes the odor measurement result obtained by the odor measuring device 115 and converts it into the odor data 105 . Specifically, the CPU 131 acquires the odor measurement result by the odor measuring device 115, calculates the difference, and obtains the odor data 105 as processed data.

In S1003, CPU 131 generates scent data containing data 101 having image data 103 and scent data 105 in a state in which image data 103 and scent data 105 are associated with each other.

In S1004, the CPU 131 compares the odor data 105 of the odor data containing data 101 with the odor data contained in the data in the database D11. Then, among the compared data, data whose odor data match or are similar are extracted from the database D11. The database D11 may be stored in the storage device 133 or may be stored in a cloud server communicably connected via the Internet. If the database D11 is stored in a cloud server, the CPU 131 can download the extracted data and store it in the storage device 133 .

In S<b>1005 , the CPU 131 can output the definitive diagnosis, symptoms, cases, and other information contained in the extracted data to the display screen (not shown) of the diagnostic apparatus 100 , for example. As a result, it is possible not only to conduct a CT examination of a subject, but also to refer to the diagnosis name, symptoms, cases, etc. of other subjects for whom odor data having similar characteristics to the subject's odor data was measured. become.

[Example 2]
As a second embodiment, a case where the composite data generation device is the portable information terminal 200 and the main data 3 is position data will be described. The mobile information terminal 200 is a mobile information terminal 200 equipped with a global positioning system (GPS) device 213, and specifically, a smartphone equipped with the GPS device 213, a tablet terminal, a mobile terminal such as a mobile phone. can. The mobile information terminal 200 includes an odor measurement device 215 in addition to the GPS device 213 . The mobile information terminal 200 may further include a display screen 211 , an imaging device 217 and an atmospheric pressure measurement device 219 .

FIG. 17 is a schematic diagram showing a mobile information terminal 200 according to the second embodiment. As shown in FIG. 17, the mobile information terminal 200 includes the odor measuring device 215, so that the odor of the ambient air in which the mobile information terminal 200 is located can be measured. The odor measurement device 215 may have the introduction port 56 described above, the ventilation ports 58 and 60, the adjustment devices 55, 57 and 59, and the like. When odor measurement device 215 is arranged inside the housing of portable information terminal 200 , introduction port 56 opens on one side of the housing of portable information terminal 200 . In that case, the ventilation ports 58 and 60 are open on a surface different from the surface on which the introduction port 56 of the housing of the portable information terminal 200 is open.

The introduction port 56 of the odor measurement device 215 may be arranged on the same plane as the imaging device 217 . Imaging device 217 is, for example, a camera. Since the introduction port 56 of the odor measurement device 215 is arranged on the same plane as the imaging device 217, it is highly possible that the image captured by the imaging device 217 includes the source of the odor to be measured.

The odor measuring device 215 has a plurality of sensor elements 255a, 255b, 255c. Although FIG. 18 shows only three sensor elements 255a, 255b, and 255c for convenience of explanation, the number of sensor elements is not limited to three.

FIG. 18 is a block diagram schematically showing the internal configuration of the mobile information terminal 200 of the second embodiment. As shown in FIG. 18, in the portable information terminal 200, an arithmetic processing unit (CPU) 231, a storage device 233, a GPS device 213, an imaging device 217, an atmospheric pressure measurement device 219, an odor measurement device 215, are connected so that they can communicate with each other. The CPU 231 can control operations of the storage device 233 , the GPS device 213 , the imaging device 217 , the atmospheric pressure measurement device 219 and the odor measurement device 215 . The storage device 233 stores a program P21 and a database D21. By executing the program P21 by the CPU 231, the portable information terminal 200 can exhibit the function of acquiring the position data 203, the function of capturing an image, the function of measuring the air pressure, the function of measuring the smell of the ambient air, and the like. can.

The CPU 231 controls the odor measuring device 215 by executing the program P21, and acquires atmospheric air using the odor measuring device 215 at the same time as the acquisition of the position data 203 described above, or before or after the acquisition of the position data 203. , its odor can be measured. As the odor measurement device 215, the odor sensor 10 described above can be used. The CPU 231 can generate the odor data 205 based on the measurement results obtained by the odor measuring device 215 by executing the program P21.

The odor measuring device 215 has an arithmetic processing unit (CPU) 251 and a storage device 253 in addition to the sensor elements 255a, 255b, 255c and the like. The CPU 251 can acquire measurement results obtained by the sensor elements 255 a , 255 b , 255 c and the like and store them in the storage device 253 . Note that the CPU 251 may transfer the measurement result to the CPU 231 of the mobile information terminal 200 and store it in the storage device 233 without storing the measurement result in the storage device 253 .

The storage device 233 stores a database D21 in which various types of information such as latitude and longitude position data 203, atmospheric pressure information, odor data, measurement time, and image data are stored in association with each other. The odor data 205 stored in the database D21 may be the odor measurement result itself (raw data) obtained by the odor measurement device 215, or the odor data 5 processed based on the odor measurement result. good. By associating the odor data 205 with the various information acquired by the mobile information terminal 200 in this way, it is possible to associate the odor with the location information. For example, by expanding the database D21 in which odors and positional information are associated, it is possible to grasp how the odors measured using the mobile information terminal 200 in a specific place are distributed on the map. become able to. In addition, it is possible to grasp the place where an odor having the same or similar odor data as the odor data measured by the mobile information terminal 200 or the like is generated by searching using the odor data as a key. Become.

By associating the odor data 205 and the position data 203 with the air pressure information measured by the air pressure measurement device 219, it is possible to understand under what pressure environment the odor data measured by the odor measurement device 215 are measured. can be done. Further, by associating the image data captured by the imaging device 217 with the odor data 205 and the position data 203, it is possible to refer to the image of the environment in which environment the measured odor data was generated. can. For example, by associating image data, it may be possible to obtain an image of the source of the odor.

FIG. 19 is the database D21 of the second embodiment. The database D21 stores odor data 205, longitude, latitude, atmospheric pressure, measurement time, and image data in association with the position data 203 acquired by the GPS device 213. FIG. The database D21 may be stored not only in the storage device 233 but also in a cloud server to which the mobile information terminal 200 is connected via the Internet. When the database D21 is stored in the cloud server, the portable information terminal 200 may directly store each piece of information stored in the database D21 in the database D21 on the cloud server without storing it in the storage device 233. .

Here, a flow of processing for acquiring the position data 203 and the like and acquiring the odor data 205 in the mobile information terminal 200 will be described with reference to FIG. FIG. 20 is a flow chart showing processing of the mobile information terminal 200 of the second embodiment. In S2001, the portable information terminal 200 starts acquisition of the position data 203 by the GPS device 213, and the smell measurement device 215 starts smell measurement. Specifically, the CPU 231 controls the GPS device 213 and records the position data 203 . Note that the odor measurement by the odor measurement device 115 and the acquisition of the position data 203 may be started without waiting for the completion of one process, and the operation time periods may overlap. Also, the acquisition of the position data 203 may be started prior to the odor measurement. At this time, the atmospheric pressure measurement by the atmospheric pressure measurement device 219 and the image capturing by the imaging device 217 may be performed at the same time as or before or after the acquisition of the position data 203 or the odor survey.

In S<b>2002 , the CPU 231 processes the odor measurement results obtained by the odor measuring device 215 to obtain the odor data 205 . Specifically, the CPU 231 acquires the odor measurement result by the odor measuring device 215, calculates the difference, and obtains the odor data 205 as processed data.

In S2003, CPU 231 generates scent data containing data 201 having position data 203 and scent data 205 in a state in which position data 203 and scent data 205 are associated with each other.

In S2004, the CPU 231 compares the odor data 205 of the odor data containing data 201 with the odor data contained in the data in the database D21. Then, among the compared data, data whose odor data match or are similar are extracted from the database D21. The database D21 may be stored in the storage device 233 or may be stored in a cloud server communicably connected via the Internet. If the database D21 is stored in a cloud server, the CPU 231 can download the extracted data and store it in the storage device 233. FIG.

In S2005, the CPU 231 can output information such as the position, air pressure, image, and measurement time included in the extracted data to the display screen 211 of the mobile information terminal 200, for example.

[Example 3]
As Example 3, a case where the composite data generation device is the moving image shooting terminal 300 and the main data 3 is moving image data will be described. The moving image capturing terminal 300 is a moving image capturing terminal 300 that includes a moving image capturing device 313. Specifically, it may be a video camera, a smart phone, a tablet terminal, an action camera, or the like. The moving image capturing terminal 300 includes an odor measuring device 315 in addition to the moving image capturing device 313 . The video shooting terminal 300 may further include a lens 311 and an audio recording device 317 .

FIG. 21 is a schematic diagram showing a video shooting terminal 300 according to the third embodiment. As shown in FIG. 21, the video shooting terminal 300 includes the odor measuring device 315, so that the smell of the ambient air in which the video shooting terminal 300 is located can be measured. The odor measurement device 315 may have the introduction port 56 described above, the ventilation ports 58 and 60, the adjustment devices 55, 57 and 59, and the like. When the odor measuring device 315 is arranged inside the housing of the moving image capturing terminal 300 , the introduction port 56 is open on one side of the housing of the moving image capturing terminal 300 . In that case, the ventilation ports 58 and 60 are open on a surface different from the surface on which the introduction port 56 of the housing of the moving image capturing terminal 300 is open.

The introduction port 56 of the odor measurement device 315 is preferably arranged on the same surface as the lens 311 or on a surface facing a similar direction. Since the introduction port 56 of the odor measuring device 315 is arranged in the same direction as the lens 311 or in a direction similar thereto, the odor to be measured is generated in the image captured by the moving image capturing device 313 through the lens 311 . It is more likely that the source is visible.

The odor measuring device 315 has a plurality of sensor elements 355a, 355b, 355c. Although FIG. 22 shows only three sensor elements 355a, 355b, and 355c for convenience of explanation, the number of sensor elements is not limited to three.

FIG. 22 is a block diagram schematically showing the internal configuration of the moving image capturing terminal 300 of Example 3. As shown in FIG. As shown in FIG. 22, in the moving image capturing terminal 300, an arithmetic processing unit (CPU) 331, a storage device 333, a moving image capturing device 313, an audio recording device 317, and an odor measuring device 315 communicate with each other. connected as possible. The CPU 331 can control the operations of the storage device 333 , moving image capturing device 313 , audio recording device 317 and odor measuring device 315 . The storage device 333 stores a program P31 and a database D31. By executing the program P31, the CPU 331 enables the moving image capturing terminal 300 to exhibit the function of acquiring the moving image data 303, the function of recording the audio data 307, the function of measuring the ambient air smell data 305, and the like. .

The CPU 331 controls the odor measuring device 315 by executing the program P31, and uses the odor measuring device 315 to measure the atmospheric air at the same time as, during, or before and after the moving image is captured. can be obtained and its odor can be measured. As the odor measurement device 315, the odor sensor 10 described above can be used. By executing the program P31, the CPU 331 can generate the odor data 305 based on the measurement results obtained by the odor measuring device 315. FIG.

The odor measuring device 315 has an arithmetic processing unit (CPU) 351 and a storage device 353 in addition to the sensor elements 355a, 355b, 355c and the like. The CPU 351 can acquire measurement results obtained by the sensor elements 355 a , 355 b , 355 c and the like, and store them in the storage device 353 . Note that the CPU 351 may transfer the measurement result to the CPU 331 of the video shooting terminal 300 and store it in the storage device 333 without storing the measurement result in the storage device 353 .

The storage device 333 stores a database D31 in which various types of information such as video data 303, odor data 305, and audio data 307 are stored in association with each other. The odor data 305 stored in the database D31 may be the odor measurement result itself (raw data) obtained by the odor measurement device 315, or the odor data 5 processed based on the odor measurement result. good. By associating the odor data 305 with various information acquired by the moving image capturing terminal 300 in this manner, it is possible to associate the odor with the moving image data 303 . For example, by expanding the database D31 in which odors and location information are associated, it is possible to understand under what circumstances the odors measured using the video shooting terminal 300 at a specific location occurred. will be able to Also, by searching using the odor data as a key, it is possible to grasp the situation in which the odor having the same or similar odor data as the odor data 305 measured by the moving image capturing terminal 300 or the like is generated. It becomes possible to Audio data 307 recorded using an audio recording device 317 can be associated with the odor data 305 and video data 303 .

FIG. 23 shows the database D31 of the third embodiment. The database D31 stores smell data 305, voice data 307, information on shooting date and time, etc. in association with the moving image data 303 acquired by the moving image shooting device 313. FIG. The database D31 may be stored not only in the storage device 333, but also in a cloud server to which the video shooting terminal 300 is connected via the Internet. When the database D31 is stored in the cloud server, the video shooting terminal 300 may directly store each piece of information stored in the database D31 in the database D31 on the cloud server without storing it in the storage device 333. .

Here, the flow of processing for obtaining the moving image data 303 and the like and the smell data 305 in the moving image capturing terminal 300 will be described with reference to FIG. FIG. 24 is a flow chart showing processing of the moving image capturing terminal 300 of the third embodiment. In S3001, the moving image capturing terminal 300 starts acquisition of moving image data 303 by the moving image capturing device 313, and the smell measuring device 315 starts smell measurement. Specifically, the CPU 331 controls the moving image capturing device 313 to record the moving image data 303 . Note that the smell measurement by the smell measuring device 315, the recording of the video data 303, and the recording of the audio data 307 are preferably performed simultaneously.

The odor measurement by the odor measurement device 315 can acquire the odor data 305 with a measurement interval of, for example, one second. It can be longer, such as minutes, half an hour, an hour, and so on. If it is expected that the odor of the ambient air will not change much during video recording, the number of times the odor measuring device 315 measures the odor is reduced. Odor measurement can be performed with a small number of times at any time.

In S<b>3002 , the CPU 331 processes the odor measurement result obtained by the odor measurement device 315 to obtain odor data 305 . Specifically, the CPU 331 acquires the odor measurement result by the odor measuring device 315, calculates the difference, and obtains the odor data 305 as processed data.

In S3003, the CPU 331 generates the scent data containing data 301 having the video data 303 and the scent data 305 in a state in which the video data 303 and the scent data 305 are associated with each other.

In S3004, the CPU 331 compares the odor data 305 of the odor data containing data 301 with the odor data contained in the data in the database D31. Then, among the compared data, data whose odor data match or are similar are extracted from the database D31. The database D31 may be stored in the storage device 333 or may be stored in a cloud server communicably connected via the Internet. If the database D31 is stored in a cloud server, the CPU 331 can download the extracted data and store it in the storage device 333 .

In S<b>3005 , the CPU 331 can output information such as the moving image, sound, shooting date and time included in the extracted data to, for example, a display screen (not shown) of the moving image capturing terminal 300 together with the moving image.

(Application example 1 of embodiment 3)
When a video of a dish is shot using the video shooting terminal 300, the odor measuring device 315 of the video shooting terminal 300 measures the smell emitted from the food and associates it with the video data 303 of the food. Odor data 305 is generated. Using the generated odor data 305 as a key, the database D31 is searched for odor data identical or similar to the odor data 305, and the identical or similar odor data and information such as video data associated therewith are extracted from the database D31. be done. As a result, moving image data associated with smell data that is the same as or similar to the photographed food is obtained.

For example, when the user uses the video shooting terminal 300 to shoot a video of a dish that he/she likes, the cooking video of that dish, the video of another dish having smell data similar to the smell data 305 of that dish, etc. available to the user.

(Application example 2 of embodiment 3)
The video shooting terminal 300 can be used as a security camera or the like. A security camera or the like captures images of an unspecified number of people. There is a great demand for identifying a person based on images captured by a security camera or the like. Therefore, by using the moving image capturing terminal 300 as a security camera or the like, the odor measuring device 315 can detect the body odor, perfume, or the like emitted by a person who enters the moving image capturing range. If the smell data 305 obtained by measuring the body odor of a person to be identified is stored in the database D31 or the like, the smell data 305 obtained using the moving image shooting terminal 300 can be used in addition to identifying the person based on the moving image. It is also possible to identify a person based on Therefore, it is possible to specify a person based on the smell data 305 in addition to specifying the person based on the moving image, and it is expected that the accuracy of specifying the person will be greatly improved.

For example, when searching for a lost child or a criminal, if the odor data of the lost child or criminal can be acquired in advance, the odor data is stored in the database D31 together with the video data and image data. be able to. Then, if the security camera as the moving image capturing terminal 300 captures a picture of a lost person or a criminal and acquires the smell data 305, the acquired smell data 305 and the smell data stored in the database D31 match. Alternatively, a person can be identified by approximation. At this time, it is expected that the accuracy of person identification will be improved by combining person identification from moving images captured using the moving image capturing terminal 300 and person identification from smell data.

Although the preferred embodiments of the present invention have been described above, the present invention is not limited to these, and various modifications and changes are possible within the scope of the gist thereof. For example, the present invention includes the following gists.

(Purpose 1) A data structure having a main data storage area in which main data is stored and an odor data storage area in which odor data based on measurement results of odors in the air measured by an odor sensor is stored. and

According to this, data associated with specific odor data can be searched and extracted from the set of data.

(Purpose 2) The main data storage area includes a main data ID area for storing a main data ID that indicates that the data stored in the main data storage area is the main data, and/ Alternatively, the odor data storage area includes an odor data ID area for storing an odor data ID indicating that the data stored in the odor data storage area is the odor data. It may be a data structure.

(Purpose 3) The scent data storage area may have a data structure for storing a plurality of scent data.

(Purpose 4) The odor data storage area may have a data structure having a plurality of odor data ID areas.

(Purpose 5) The odor sensor includes a plurality of sensor elements each having a substance adsorption film that adsorbs an odorant in the air and a detector, and the substance adsorption film is provided for each sensor element to detect the odor. The plurality of sensor elements having different adsorption characteristics with respect to substances output different measurement results according to the amount of adsorption of the odorant to the substance adsorption film, and the plurality of odor data are obtained from the plurality of sensor elements. may be data structures respectively corresponding to .

(Purpose 6) Even if the data structure is such that the odor data is the measurement result of the odor in the air measured by the odor sensor and is transition data indicating the temporal change of the odor in the air. good.

(Purpose 7) The transition data comprises a plurality of data sets of measured values obtained by measuring the odor in the air by the odor sensor at predetermined time intervals over a predetermined time width and the measurement times of the measured values. It may be a data structure that

(Purpose 8) The odor data is the measurement result of the odor in the air measured by the odor sensor, and the odor data is the first time or time span for indicating the temporal change of the odor in the air. The data structure may be differential data between a first measurement result of the odor in the air and a second measurement result of the odor in the air at a second time or time span.

(Purpose 9) The first measurement result is the measurement result of the odor in the air at the first time, and the second measurement result is the measurement result of the odor in the air at the second time. and wherein the time difference between the first time and the second time is at least 5 seconds.

(Purpose 10) The first measurement result is the maximum value among the measurement results of the odor in the air in the first time period, and the second measurement result is the second time period. The data structure may be a minimum value among the measurement results of the odor in the air.

(Purpose 11) The main data includes at least one of image data, video data, audio data, text data, and position data, and the main data storage area and the scent data storage area are associated with each other. It may be a data structure that contains

(Purpose 12) Composite data comprising main data generating means for generating the main data and the odor sensor, and generating composite data based on the data structure according to any one of the purports 1 to 11. Also intended as a generator.

1: Data containing odor data 3: Main data
4: Main data storage area 5: Odor data
6: Odor data storage area 7: Element data
8: element data storage area 9: element data point
10: Odor sensor 11: Sensor element
13: substance adsorption film 15: detector
17: Sensor substrate 19: Sensor surface
23: Main data ID 24: Main data ID area
25: Odor data ID 26: Odor data ID area
27: element data ID 28: element data ID area
29: Time label 30: Time label area
50: Odor measuring device
51: Arithmetic processing unit (CPU) (of the odor measuring device)
53: Storage device (of the odor measuring device)
55: Adjusting device 56: Inlet
57: Adjusting device 58: Ventilation opening
59: Adjustment device 60: Ventilation opening
100: Diagnostic device 101: Data containing odor data
103: Image data 105: Odor data
111: Gantry 112: X-ray irradiation device
113: X-ray detector 115: Odor measuring device
117: bed
131: Arithmetic processing unit (CPU) (of diagnostic device)
133: Storage device (of the diagnostic device)
151: Arithmetic processing unit (CPU) (of the odor measuring device)
153: Storage device (of the odor measuring device)
155a: sensor element 155b: sensor element
155c: Sensor element 200: Personal digital assistant
201: Data containing odor data 203: Location data
205: Odor data 211: Display screen
213: GPS device 215: Odor measuring device
217: Imaging device 219: Air pressure measuring device
231: Arithmetic processing unit (CPU) (of personal digital assistant)
233: Storage device (of personal digital assistant)
251: Arithmetic processing unit (CPU) (of the odor measuring device)
253: Storage device (of the odor measuring device)
255a: sensor element 255b: sensor element
255c: Sensor element 300: Video shooting terminal
301: Data containing odor data 303: Video data
305: Odor data 307: Voice data
311: Lens 313: Video shooting device
315: Odor measuring device 317: Voice recording device
331: Arithmetic processing unit (CPU) (of video shooting terminal)
333: Storage device (of video shooting terminal)
351: Arithmetic processing unit (CPU) (of video shooting terminal)
353: Storage device (of video shooting terminal)
355a: sensor element 355b: sensor element
355c: sensor element
D1: Measurement result database D2: Processing database
D11: Database (of Example 1)
D21: Database (of Example 2)
D31: Database (of Example 3)
P11: Program (of Example 1)
P21: Program (of Example 2)
P31: Program (of Example 3)
Pa: program

Claims (8)

A composite data generation method for generating composite data by a computer, comprising:
The data structure of the composite data is
a main data storage area in which main data is stored;
an odor data storage area for storing odor data based on measurement results of odors in the air measured by an odor sensor having a plurality of sensor elements;
the main data storage area has a main data ID indicating that the data stored in the main data storage area is the main data;
the odor data storage area has a plurality of odor data IDs indicating that the data stored in the odor data storage area is the odor data;
the plurality of odor data IDs correspond to the plurality of sensor elements,
The composite data is configured to be able to extract the main data associated with the plurality of odor data based on the plurality of odor data stored in the odor data storage area,
By said computer,
a calculating step of calculating a difference value between a first measurement result in a first time width among the measurement results and a second measurement result in a second time width among the measurement results;
a storing step of storing the difference value as the scent data in the scent data storage area;
and a repeating step of performing the calculating step and the storing step for each of the plurality of odor data corresponding to each of the plurality of sensor elements. 2. The method of generating composite data according to claim 1, wherein said odor data is a measurement result of an odor in the air measured by said odor sensor, and is transition data indicating a change in the odor in the air over time. . 3. The composite data generation method according to claim 1, wherein said main data includes at least one of image data, moving image data, audio data, text data, and position data. 4. The composite data generation method according to claim 1, wherein said first duration and said second duration overlap at least partially. 4. The composite data generation method according to claim 1, wherein said first duration and said second duration do not overlap. wherein the first measurement result is the maximum value among the measurement results of the odor in the air in the first time span;
The composite data according to any one of claims 1 to 5 , wherein the second measurement result is the minimum value among the measurement results of the odor in the air in the second time span. generation method. the computer;
main data generation means for generating the main data;
and the odor sensor,
Composite data generation, wherein the computer generates the composite data by executing the calculating step, the storing step, and the repeating step according to any one of claims 1 to 6. Device. The plurality of sensor elements are
comprising a substance adsorption film that adsorbs odorants in the air and a detector,
the substance adsorption film has different adsorption characteristics for the odorant for each of the plurality of sensor elements,
8. The composite data generating device according to claim 7, which outputs different measurement results according to the amount of adsorption of said odorant to said substance adsorption film.

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